Detection of Thoracic Diseases using Deep Learning

Detection of Thoracic Diseases using Deep Learning

The study of using deep learning for detection of various thoracic diseases has been an active and challenging research area. Chest X-rays are currently the most common and globally used radiology practices for detecting thoracic diseases. Patients suffering from thoracic diseases need to take Chest...

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Main Authors: Palani Salome, Kulkarni Arya, Kochara Abishai, M Kiruthika
Format: Article
Language:English
Published: EDP Sciences 2020-01-01
Series:ITM Web of Conferences
Online Access:https://www.itm-conferences.org/articles/itmconf/pdf/2020/02/itmconf_icacc2020_03024.pdf
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spelling doaj-a417b8da564c4a9f8309e8e8308985b52021-04-02T13:20:15ZengEDP SciencesITM Web of Conferences2271-20972020-01-01320302410.1051/itmconf/20203203024itmconf_icacc2020_03024Detection of Thoracic Diseases using Deep LearningPalani Salome0Kulkarni Arya1Kochara Abishai2M Kiruthika3B.E student, Department of Computer Engineering, Agnel Charities’ Fr. C. Rodrigues Institute of Technology, VashiB.E student, Department of Computer Engineering, Agnel Charities’ Fr. C. Rodrigues Institute of Technology, VashiB.E student, Department of Computer Engineering, Agnel Charities’ Fr. C. Rodrigues Institute of Technology, VashiAssociate Professor, Department of Computer Engineering, Agnel Charities’ Fr. C. Rodrigues Institute of Technology, VashiThe study of using deep learning for detection of various thoracic diseases has been an active and challenging research area. Chest X-rays are currently the most common and globally used radiology practices for detecting thoracic diseases. Patients suffering from thoracic diseases need to take Chest X-Rays which are read by radiologists and a report is generated by them. However, today with the increase in the number of thoracic patients, a quick method to classify the disease and generate the report has become necessary. Also, patient history has to be considered for diagnosis. This paper offers a comparative study on the various deep learning techniques that can process chest x-rays and are capable of detecting the different thoracic diseases. Also, a technique has been proposed to classify 14 diseases namely Atelectasis, Cardiomegaly, Consolidation, Edema, Effusion, Emphysema, Fibrosis, Hernia, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, Pleural thickening based on the given X-rays using Residual Neural Network.https://www.itm-conferences.org/articles/itmconf/pdf/2020/02/itmconf_icacc2020_03024.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Palani Salome
Kulkarni Arya
Kochara Abishai
M Kiruthika
spellingShingle Palani Salome
Kulkarni Arya
Kochara Abishai
M Kiruthika
Detection of Thoracic Diseases using Deep Learning
ITM Web of Conferences
author_facet Palani Salome
Kulkarni Arya
Kochara Abishai
M Kiruthika
author_sort Palani Salome
title Detection of Thoracic Diseases using Deep Learning
title_short Detection of Thoracic Diseases using Deep Learning
title_full Detection of Thoracic Diseases using Deep Learning
title_fullStr Detection of Thoracic Diseases using Deep Learning
title_full_unstemmed Detection of Thoracic Diseases using Deep Learning
title_sort detection of thoracic diseases using deep learning
publisher EDP Sciences
series ITM Web of Conferences
issn 2271-2097
publishDate 2020-01-01
description The study of using deep learning for detection of various thoracic diseases has been an active and challenging research area. Chest X-rays are currently the most common and globally used radiology practices for detecting thoracic diseases. Patients suffering from thoracic diseases need to take Chest X-Rays which are read by radiologists and a report is generated by them. However, today with the increase in the number of thoracic patients, a quick method to classify the disease and generate the report has become necessary. Also, patient history has to be considered for diagnosis. This paper offers a comparative study on the various deep learning techniques that can process chest x-rays and are capable of detecting the different thoracic diseases. Also, a technique has been proposed to classify 14 diseases namely Atelectasis, Cardiomegaly, Consolidation, Edema, Effusion, Emphysema, Fibrosis, Hernia, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, Pleural thickening based on the given X-rays using Residual Neural Network.
url https://www.itm-conferences.org/articles/itmconf/pdf/2020/02/itmconf_icacc2020_03024.pdf
work_keys_str_mv AT palanisalome detectionofthoracicdiseasesusingdeeplearning
AT kulkarniarya detectionofthoracicdiseasesusingdeeplearning
AT kocharaabishai detectionofthoracicdiseasesusingdeeplearning
AT mkiruthika detectionofthoracicdiseasesusingdeeplearning
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